\label{ch1:sec:01}
%The wireless networking has received more attention and fast growth in the last decade.
In the last decade, wireless networking has became a major component of the global network infrastructure.
-More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and wireless local-area networks
+More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and Personal Digital Assistants (PDAs) have led to develop different infrastructures of wireless networks. Wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and Wireless Local-Area Networks
(WLANs); and Infrastructureless networks that are constructed dynamically by the cooperation of the wireless nodes in the network, where each node is capable of sending packets and taking decisions based on the network status. Examples of such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.
\begin{figure}[h!]
%WSNs are considered as one of the most researched fields in the last decade due to the extensive research in this discipline.
Wireless Sensor Networks (WSNs) represent a special case of the Ad Hoc networks, resulting from recent advances in wireless networking, Micro-Electro-Mechanical Systems (MEMS), and embedded computing technologies, which have led to construct low-cost, small-sized, and low-power sensor nodes.
%These sensor nodes can perform detection, computation, and data communication of surrounding environment.
-A WSN includes a large number of sensor nodes that can sense, process, and transmit data over a wireless communication. The sensor nodes communicate with each other by using multi-hop wireless communications and cooperate together to monitor the area of interest. Measured data are reported to a monitoring center called base station or sink for further analysis~\cite{ref1,ref2}. The WSN receives the orders from the end user by means of the sink. These orders specify data aggregation, computation, and delivery missions to wireless sensor nodes, after that the sensed measurements are received from the WSN by the sink~\cite{ref3}. The cooperation among wireless sensor nodes in WSNs has several advantages over traditional wireless ad-hoc networks, like self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability~\cite{ref5}.
+A WSN includes a large number of sensor nodes that can sense, process, and transmit data over a wireless communication. The sensor nodes communicate with each other by using multi-hop wireless communications and cooperate together to monitor the area of interest. Measured data are reported to a monitoring center called base station or sink for further analysis~\cite{ref1,ref2}. A WSN receives the orders from the end user by means of the sink. These orders specify data aggregation, computation, and delivery missions to wireless sensor nodes, after that the sensed measurements are received from the WSN by the sink~\cite{ref3}. The cooperation among wireless sensor nodes in WSNs has several advantages over traditional wireless ad-hoc networks, like self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability~\cite{ref5}.
\section{Architecture}
\label{ch1:sec:02}
-A typical WSN architecture consists of in a set of a huge number of wireless sensor nodes, which are capable of sensing the surrounded physical phenomenon such as fire in the forest (see~figure~\ref{wsn}), and then send the sensed data to a sink node. One or more sink in WSN are responsible for collecting and processing the received sensed data, and making them available through the Internet to the end-user.
+A typical WSN architecture consists in a set of a huge number of wireless sensor nodes, which are capable of sensing the surrounded physical phenomenon such as fire in the forest (see~Figure~\ref{wsn}), and then send the sensed data to a sink node. One or more sinks in WSN are responsible for collecting and processing the received sensed data, and making them available through the Internet to the end-user.
The basic element is a wireless sensor node that is composed of four major units~\cite{ref17,ref18}: sensing, computation, communication, and power.
%In addition, there are three optional units, which can be combined with the sensor node such as localization system, mobilizer, and power generator.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch1/twsn2.pdf}
+\includegraphics[scale=0.52]{Figures/ch1/twsn2.pdf}
\caption{ Components of a typical wireless sensor node.}
\label{twsn}
\end{figure}
\item \textbf{Computation Unit:} The main purpose of this unit is to manage and manipulate the instructions that are related to sensing, communication, and self-organization. This allows the sensor node to cooperate with other sensor nodes in order to perform the allocated sensing tasks. It is composed of a processor chip, an active short-term memory for storing the sensed data, an internal flash memory for storing program instructions, and an internal timer.
-\item \textbf{Communication Unit:} It is responsible for all data transmission and reception done by the sensor node, which are performed by the transceiver circuitry. A transceiver circuit is composed of a mixer, frequency synthesizer, voltage-controlled oscillator (VCO), phase-locked loop (PLL), demodulator, and power amplifiers. All these components consume valuable power~\cite{ref19}.
+\item \textbf{Communication Unit:} It is responsible for all data transmission and reception done by the sensor node, which are performed by the transceiver circuitry. A transceiver circuit is composed of a mixer, frequency synthesizer, Voltage-Controlled Oscillator (VCO), Phase-Locked Loop (PLL), demodulator, and power amplifiers. All these components consume valuable power~\cite{ref19}.
\item \textbf{Power Unit:} This unit represents the most significant part of a sensor node. It supplies the other units by the needed power.
\end{enumerate}
-Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in figure~\ref{twsn}.
+Furthermore, additional components can be incorporated into wireless sensor node according to the application requirements, such as a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in Figure~\ref{twsn}.
\begin{enumerate} [(I)]
\label{wsn}
\end{figure}
-The sensor node use software layer that logically locates between the node's hardware and the application called, An operating system (OS)~\cite{ref18}. OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
+Sensor nodes use a software layer called, Operating System (OS), which is logically between the node's hardware and the application layer~\cite{ref18}. The OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS is commonly used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
\section{Types of Wireless Sensor Networks}
\label{ch1:sec:03}
-According to the physical phenomena for which the WSN is developed, WSNs can be deployed on the ground, underground, or underwater. WSNs suffer from different conditions and challenges. WSNs can be classified into six types, thus among which five types are presented in~\cite{ref4,ref5}. Figure~\ref{wsnt} gives examples of different WSNs types.
+According to the physical phenomena for which the WSN is developed, WSNs can be deployed on the ground, underground, or underwater. WSNs suffer from different conditions and challenges. WSNs can be classified into six types, among which five types are presented in~\cite{ref4,ref5}. Figure~\ref{wsnt} gives examples of different WSNs types.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf}
\begin{enumerate}[(I)]
\item \textbf{Terrestrial WSNs:}
-The wireless sensor nodes are deployed over the land constructing a network of hundred to thousand of sensor devices. Several applications use terrestrial WSNs for physical environmental sensing and monitoring, industrial monitoring, and surface explorations. The main challenges in this type of WSNs are to ensure coverage and connectivity with removing redundancy, energy-efficient routing, data communication reduction, balancing energy consumption, energy-efficient data aggregation.
+Wireless sensor nodes are deployed over the land constructing a network of hundred to thousand of sensor devices. Several applications use terrestrial WSNs for physical environmental sensing and monitoring, industrial monitoring, and surface explorations. The main challenges in this type of WSNs are to ensure coverage and connectivity with removing redundancy, energy-efficient routing, data communication reduction, balancing energy consumption, energy-efficient data aggregation.
%This dissertation focuses on this type of WSNs.
\item \textbf{Underground WSNs:}
Nodes are deployed over caves, mines, or underground and communicate through soil~\cite{ref9,ref10}. The most important applications in underground WSNs are structural monitoring, agriculture monitoring, landscape management, underground environment monitoring of soil, water or mineral and military border monitoring. The essential challenges of underground WSNs are the high levels of attenuation and signal loss in communication. Therefore, it needs a certain type of devices able to provide a robust wireless underground communication. The risk on these devices comes from unsuitable underground conditions, replacing or recharging the battery seems to be impossible, and the WSN deployment is expensive.
\item \textbf{Underwater WSNs:}
-This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges led to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration.
+This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges lead to look for energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration. \\
\item \textbf{Multimedia WSNs:}
They consist of inexpensive wireless sensor nodes supplied with CMOS (Complementary Metal-Oxide-Silicon) cameras or microphones devices. The nodes are deployed in a pre-guided way to ensure the coverage. Multimedia WSN is capable of retrieving and storing audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}. Multimedia WSN contributed in improving some existing WSN applications such as tracking and monitoring. The main challenges in multimedia WSN include: the processing, filtering, and compressing of multimedia data; the requested bandwidth and high energy consumption; Quality-of-Service provisioning is very difficult because of the link capacity and delays; it should combine different wireless techniques; energy-efficient cross-layer design; it needs flexible architecture to support various applications; and the deployment is based on the multimedia devices coverage.
\item \textbf{Mobile WSNs:}
Such a network is composed of mobile sensor nodes that can move autonomously so as to self-organize~\cite{ref16}.
%self-moving and reacting for the physical phenomena~\cite{ref16}. The mobile sensor node is self-organized and it is capable of replacing its position autonomously. In addition, it is able to sense, process, and communicate with other mobile sensors.
-Many challenges should be faced in mobile WSNs such as maintaining a sufficient sensing coverage and connectivity; the self-organization; the navigation and control of mobile sensors; mobility management; processing in WSN; location determination with mobility; and minimizing the energy consumption especially during the movement. The mobile WSN applications are environment, habitat, and underwater monitoring; target tracking; military surveillance; search and rescue. The mobility in WSNs improves the network coverage of the monitored area especially after the initial random deployment becuase they can relocate themselves to fill coverage holes \cite{ref234,ref235}.
+Many challenges should be faced in mobile WSNs such as maintaining a sufficient sensing coverage and connectivity; the self-organization; the navigation and control of mobile sensors; mobility management; processing in WSN; location determination with mobility; and minimizing the energy consumption especially during the movement. The mobile WSN applications are environment, habitat, and underwater monitoring; target tracking; military surveillance; search and rescue. The mobility in WSNs improves the network coverage of the monitored area especially after the initial random deployment because they can relocate themselves to fill coverage holes \cite{ref234,ref235}.
\item \textbf{Flying WSNs:}
This kind of WSN consists of low-cost wireless sensor nodes, which are embedded in Micro Aerial Vehicles (MAVs). They can fly autonomously or can be operated remotely without intervention of any human personnel~\cite{ref6,ref7}. The general objective of this type of WSN is to retrieve information from some inaccessible locations. For example, establishing an ad hoc network connection between rescuers and disaster victims over airborne relays or surveying an area from the air. A flying WSN provides a remote sensing and wireless networking platform that collect the data from local sensors or other sources, and send the collected information over airborne wireless relays to a ground station. Using Flying WSNs have led to new developments for both military and civilian applications due to their flexibility, versatility, easy installation, and the operating low-cost \cite{ref8}. The applications are search and destroy operations, disaster monitoring, relay for ad hoc networks, wind estimation, managing wildfire, border surveillance, remote sensing, and traffic monitoring. The main challenges are constructing a lightweight MAV which is capable of flight; the wireless communication; designing software protocols to achieve semi-autonomous flight; and combining all the subsystems like propulsion, flight control, and wireless networking into a flying WSN.
\section{Applications}
\label{ch1:sec:04}
%\indent The fast development in WSNs has been led to study their different characteristics extensively. However, the WSN is concentrated on various applications.
-In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in figure~\ref{WSNAP}.
+In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}.
\begin{figure}[h!]
\centering
\begin{enumerate}[(I)]
-\item \textbf{Health-care Applications:} There is an increasing interest and extensive research in this domain. Two types of health-care systems are recognized~\cite{ref22}: vital status monitoring and remote health-care surveillance. In vital status monitoring applications, sick persons are wearing the sensors in order to oversee their health state and to allow medical staff to monitor and control the patient's status expeditiously. The most general used vital signs are ECG, pulse oximetry, body temperature, heart rate, and blood pressure~\cite{ref27}. These applications include mass-casualty disaster monitoring, vital sign monitoring in hospitals, and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance refers to health services that do not require continuous existence of health care. These applications include elderly monitoring, providing support to a physically impaired person, gather clinically relevant information for rehabilitation supervision~\cite{ref28}, location tracking, and medication intake monitoring~\cite{ref27}.
+\item \textbf{Health-care Applications:} There is an increasing interest and extensive research in this domain. Two types of health-care systems are recognized~\cite{ref22}: vital status monitoring and remote health-care surveillance. In vital status monitoring applications, sick persons are wearing the sensors in order to oversee their health state and to allow medical staff to monitor and control the patient's status expeditiously. The most general used vital signs are electrocardiogram (ECG), pulse oximetry, body temperature, heart rate, and blood pressure~\cite{ref27}. These applications include mass-casualty disaster monitoring, vital sign monitoring in hospitals, and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance refers to health services that do not require continuous existence of health care. These applications include elderly monitoring, providing support to a physically impaired person, gather clinically relevant information for rehabilitation supervision~\cite{ref28}, location tracking, and medication intake monitoring~\cite{ref27}.
\item \textbf{ Environment and agriculture Applications}
-\indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring.
-
-\indent Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and create a decision support system \cite{ref22}.
-The wireless sensors can be used in agricultural services like Irrigation, fertilization, pest control, animal and pastures monitoring, horticulture (e.g., greenhouse and viticulture)~\cite{ref30}. For instance, in cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).
-
+\indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring. Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and create a decision support system \cite{ref22}.
+The wireless sensors can be used in agricultural services like irrigation, fertilization, pest control, animal and pastures monitoring, horticulture (e.g., greenhouse and viticulture)~\cite{ref30}. For instance, in cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).
\indent Various WSN applications for environmental monitoring have been used in coastline erosion, air quality monitoring, safe drinking water, and contamination control~\cite{ref30,ref22}.
%\indent In cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).
WSNs can be incorporated into military command, control, communication, computing, intelligence,
surveillance, reconnaissance, and targeting systems. It permits to estimate the unexpected events such as natural disasters and threats; military surveillance to the battlefield, enemy forces, battle damage, and targeting; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}.
-\indent According to figure~\ref{WSNAP}, the public safety and military applications can be categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are wore by the agents and the WSN devoted to the security of the team activities. During the work of the team, the leader will observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
+\indent According to Figure~\ref{WSNAP}, the public safety and military applications can be categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are worn by the agents and the WSN devoted to the security of the team activities. During the work of the team, the leader will observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include surveillance and target tracking; emergency navigation; fire detection in a building; structural health monitoring; and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
\item \textbf{Transportation Systems Applications:}
\item \textbf{Industry Applications: Manufacturing and Smart Grids:}
-The most significant goal for many companies is the automation of controlling and monitoring systems in many applications such as manufacturing, water treatment, electrical power distribution, and oil and gas refining. In that case WSNs are incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}.A SCADA system is a computer software by which industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory, to detect of liquid/gas leakages, and for inventory management. These applications are needed for precise monitoring of temperature, shock, and noise factors in remote locations such as tanks, turbine engines, or pipelines. In Smart Grids, the goal is to supervise the power supply and depletion operation. The main applications in smart grid include: sensing the relevant parameters affecting power output (pressure, humidity, wind orientation, radiation, etc.); control of turbines, motors and underground cables; home energy management; and remote detection of faulty components.
+The most significant goal for many companies is the automation of controlling and monitoring systems in many applications such as manufacturing, water treatment, electrical power distribution, and oil and gas refining. In that case WSNs are incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}. A SCADA system is a computer software by which industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory, to detect of liquid/gas leakages, and for inventory management. These applications are needed for precise monitoring of temperature, shock, and noise factors in remote locations such as tanks, turbine engines, or pipelines. In Smart Grids, the goal is to supervise the power supply and depletion operation. The main applications in smart grid include: sensing the relevant parameters affecting power output (pressure, humidity, wind orientation, radiation, etc.); control of turbines, motors and underground cables; home energy management; and remote detection of faulty components.
\end{enumerate}
%\section{Protocol Design Requirements}
\item \textbf{Routing:} It represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication lead to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of the sensor node during taking the decision to route the packet to the next hop toward the destination. This participates in energy conservation and balancing among the sensor nodes in WSNs.
\item \textbf{Autonomous and Distributed Management:}
-Since the nature of many WSN applications induce a deployment in a remote or hostile environment, it is important that the wireless sensor nodes work in an autonomous and distributed way to communicate and cooperate, without any human intervention since the maintenance or the repair may be difficult. %The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; moreover, it does not give the optimal solution. Therefore, the main challenge is how to apply a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
+Since the nature of many WSN applications induces a deployment in a remote or hostile environment, it is important that the wireless sensor nodes work in an autonomous and distributed way to communicate and cooperate, without any human intervention since the maintenance or the repair may be difficult. %The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; moreover, it does not give the optimal solution. Therefore, the main challenge is how to apply a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
-\item \textbf{Scalability:} Many physical phenomenons require the deployment of a dense WSN. A large number of sensor nodes maybe needed for different reasons such as the huge size of the sensed area, the reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols for WSNs are scalable for these large number of sensor nodes in order to achieve their tasks efficiently.
+\item \textbf{Scalability:} Many physical phenomenons require the deployment of a dense WSN. A large number of sensor nodes may be needed for different reasons such as the huge size of the sensed area, the reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols for WSNs are scalable for these large number of sensor nodes in order to achieve their tasks efficiently.
\item \textbf{Reliability:} Many applications require high quality of services, connectivity, routing, data aggregation, etc. These applications need to deploy a large number of inexpensive sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to failure and this will affect the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.
\item \textbf{Data Management:}
%It represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs.
-The main task of a WSN after deploying the sensor nodes in the target environment that need to be monitored, is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN and since every sensor node want to transmit its sensed data to the base station; there is a large amount of data that need to be managed, processed, and routed to the sink. Obviously, a suitable data management is required to minimize the corresponding energy consumption.
+The main task of a WSN after deploying the sensor nodes in the target environment that need to be monitored, is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN and since each sensor node wants to transmit its sensed data to the base station; there is a large amount of data that need to be managed, processed, and routed to the sink. Obviously, a suitable data management is required to minimize the corresponding energy consumption.
\item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. An information is susceptible to malicious intrusions and hacker attacks. As a consequence, it is necessary to provide energy efficient schemes to protect this information during the operation of WSNs.
\section{Energy-Efficient Mechanisms of a working WSN}
\label{ch1:sec:06}
-\indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in figure~\ref{emwsn}.
+\indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in Figure~\ref{emwsn}.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps}
\indent In this strategy, the wireless sensor nodes are grouped into several groups called clusters. Each group of wireless sensor nodes is managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility for managing the activities of the wireless sensor nodes in the cluster and it communicates and coordinates with other cluster heads or with the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
\begin{enumerate}[(a)]
-\item Grouping wireless sensor nodes into clusters led to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
+\item Grouping wireless sensor nodes into clusters lead to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
\item Minimizing the energy-hungry operations such as collaboration and aggregation to the cluster head.
%\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
\item The continuous changing of cluster head according to the residual energy led to balance the energy consumption among wireless sensor nodes inside the cluster.
\subsubsection{Wake up Scheduling Schemes}
-\indent This section describes the scheduling schemes from the point of view of schedule composition process and the framework of the wake-up schedule. In these scheduling schemes, the wake-up interval refers to the period of time at which the radio unit is turned on to send or receive packets. On the other hand, the sleep interval refers to a period of time at which the radio unit is turned off so as to save the energy of node. Some schemes divide the time into equal length durations of time and are called slotted schemes. Other schemes work with the time in a continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. A wake-up schedule represents a set of a wake-up and sleep intervals which are produced for one period. This schedule is replicated for each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal is to permit to exchange data among the wireless sensor nodes during the wake-up interval. As shown in figure~\ref{wsns}, the requirement for synchronization categorizes the wake-up scheduling into three categories~\cite{ref57}:
+\indent This section describes the scheduling schemes from the point of view of schedule composition process and the framework of the wake-up schedule. In these scheduling schemes, the wake-up interval refers to the period of time at which the radio unit is turned on to send or receive packets. On the other hand, the sleep interval refers to a period of time at which the radio unit is turned off so as to save the energy of node. Some schemes divide the time into equal length durations of time and are called slotted schemes. Other schemes work with the time in a continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. A wake-up schedule represents a set of a wake-up and sleep intervals which are produced for one period. This schedule is replicated for each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal is to permit to exchange data among the wireless sensor nodes during the wake-up interval. As shown in Figure~\ref{wsns}, the requirement for synchronization categorizes the wake-up scheduling into three categories~\cite{ref57}:
\begin{enumerate} [(I)]
%On the other hand, the aperiodic schemes do not apply the periodic schedule.
\begin{enumerate} [(A)]
-\item The periodic wakeup scheduling schemes work either in slotted and unslotted way, where the period is divided into equal-length slots in the slotted schemes. The major challenge in periodic wakeup scheduling is to select and activate the best time interval(s) for a period so that an active wireless sensor node performs the communication (sending and receiving). This is from point of view of wireless sensor node, whilst from the standpoint of the WSN, choosing the time intervals through the wireless sensor nodes to satisfy a certain performance factor seems to be hard task. This level of performance can be carried out with the cooperation among the sensor nodes in WSN to produce the wake-up schedule. The periodic wakeup scheduling schemes are classified into five groups based on the degree of a cooperation~\cite{ref57}:
+\item The periodic wakeup scheduling schemes work either in slotted and unslotted way, where the period is divided into equal-length slots in the slotted schemes. The major challenge in periodic wakeup scheduling is to select and activate the best time interval(s) for a period so that an active wireless sensor node performs the communication (sending and receiving). This is from the point of view of wireless sensor node, whilst from the standpoint of the WSN, choosing the time intervals through the wireless sensor nodes to satisfy a certain performance factor seems to be hard task. This level of performance can be carried out with the cooperation among the sensor nodes in WSN to produce the wake-up schedule. The periodic wakeup scheduling schemes are classified into five groups based on the degree of a cooperation~\cite{ref57}:
\begin{enumerate} [(i)]
\item Neighbor-coordinated is a scheme in which a wireless sensor node generates its own wake-up schedule taking into consideration the wake-up schedules of its neighbor sensor nodes.
\item \textbf{Asynchronous Schemes:}
%The time among the wireless sensor nodes does not need synchronization.
-The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no shared wake up schedules to be exchanged or saved in the memory. Therefore, exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
+A wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no shared wake up schedules to be exchanged or saved in the memory. Therefore, exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
\begin{enumerate} [(A)]
\item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send. If the receiving sensor node is hearing the special frame during one of its wake up intervals, the receiving node waits for sending the data frame by sender to receive it. The major advantage of these schemes is the low memory and processing requirements whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
\subsubsection{Topology Control Schemes}
-\indent The topology control schemes deal with the redundancy in WSNs. The WSN is always deployed with high density and in a random way, where a large number of wireless sensor nodes are usually supposed to be thrown by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network. By putting the redundant sensor nodes into sleep mode, the idea is to benefit from the saved energy later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off, and when. The topology control schemes have been classified into two categories~\cite{ref56}:
+\indent The topology control schemes deal with the redundancy in WSNs. The WSN is sometimes deployed with high density and in a random way, where a large number of wireless sensor nodes are usually supposed to be thrown by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network. By putting the redundant sensor nodes into sleep mode, the idea is to benefit from the saved energy later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off, and when. The topology control schemes have been classified into two categories~\cite{ref56}:
\begin{enumerate} [(I)]
\item \textbf{Location Driven Protocols:} Wireless sensor nodes are turned on or off based on their location; for example, Geographical Adaptive Fidelity (GAF) protocol~\cite{GAF}.
\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.
\begin{enumerate} [i)]
-\item{Energy Harvesting} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
+\item{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
-\item{Wireless Charging}In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\item{Wireless Charging:} In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
\end{enumerate}
\indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
\begin{enumerate}[(A)]
-\item \textbf{Binary Disc Sensing Model:}
-It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, the sensor node is not able to detect any point that is outside its sensing range. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow
+\item \textbf{Binary Disc Sensing Model:} It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, the sensor node is not able to detect any point that is outside its sensing range. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow
\begin{equation}
C_{xy}\left(s_i \right) = \left \{
\begin{array}{l l}
where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$ denotes the Euclidean distance between sensor node $s_i$ and P.
-\item \textbf{Probabilistic Sensing Model}
-In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. Equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by the sensor node $s_i$ as follow
+\item \textbf{Probabilistic Sensing Model:} In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. Equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by the sensor node $s_i$ as follow
\begin{equation}
C_{xy}\left(s_i \right) = \left \{
\end{enumerate}
-The coverage protocols proposed in this dissertation use the binary disc sensing model for each wireless sensor node in a WSN because it is widely used in the literature. Moreover, it is easy to formulate the linear programs with it, where as the probabilistic model is more complex and it is difficult to use it to create integer programs.
+The coverage protocols proposed in this dissertation use the binary disc sensing model for each wireless sensor node in a WSN because it is widely used in the literature. Moreover, it is easy to formulate linear programs with it, whereas the probabilistic model is more complex. % and it is difficult to use it to create integer programs.
%The coverage protocols have proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
\indent Several design issues should be considered in order to produce solutions for the coverage problems in WSNs. These design issues can be classified into~\cite{ref103}:
\begin{enumerate}[(i)]
-\item $\textbf{Coverage Type}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a breach of a barrier?
+\item $\textbf{Coverage Type:}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a barrier breach?
-\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved for a small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or when the area of interest is inaccessible or hostile, a random placement is the choice. The sensor network can be either dense or sparse. On the one hand, the dense deployment is preferable when it is necessary to provide a security robustness in WSNs. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a low number of sensor nodes.
+\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved in a friendly environment with a small number of sensor nodes. The random placement is preferred for a large number of sensor nodes or when the area of interest is inaccessible or hostile. The sensor network can be either dense or sparse. On the one hand, the dense deployment is preferable when it is necessary to provide a security robustness in WSNs. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a low number of sensor nodes.
-\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. A point in the sensing field is said to be K-coverage by at least K sensor nodes. Some applications need a high reliability to achieve their tasks. Therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
+\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. A point in the sensing field is said to be K-covered if it is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks. Therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
\item $\textbf{Coverage Ratio}$ is the percentage of the sensing field that fulfills the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage. Otherwise, it is said as partial coverage.
-\item $\textbf{Network Connectivity}$ ensures the existence of a path from any sensor node in WSN to the sink. A connected WSN ensures the sending of the sending the sensed data from one sensor node to another sensor node directly toward the sink.
+\item $\textbf{Network Connectivity}$ ensures the existence of a path from any sensor node in WSN to the sink. A connected WSN ensures the sending of the sensed data from one sensor node to another sensor node directly toward the sink.
%It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.
-Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
+%Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
-\item $\textbf{Activity based Scheduling}$ schedules the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself, using only local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
-This dissertation deals with activity based scheduling to ensure the best coverage.
+\item $\textbf{Activity based Scheduling}$ schedules the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in which states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself, using only local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
+\textbf{This dissertation deals with activity based scheduling to ensure the best coverage}.
\end{enumerate}
\begin{enumerate}[(i)]
-\item Computation: processing needed for executing any algorithm inside the sensor node. The processing that is required to physical communication and networking protocols is included in reception and transmission.
+\item Computation: processing needed to execute any algorithm inside the sensor node. The processing that is required to physical communication and networking protocols is included in reception and transmission.
\item Transmission: processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.
\label{RDM}
\end{figure}
-\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes energy to run the radio electronics, as described in figure~\ref{RDM}. The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold ($d_0$), the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$). Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a K-bit packet across a distance d, the radio expends
+\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes energy to run the radio electronics, as described in Figure~\ref{RDM}. The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold ($d_0$), the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$). Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a K-bit packet across a distance d, the radio is
\begin{equation}
\label{eq3-ch1}
\end{equation}
-while to receive a K-bit packet, the radio expends
+while to receive a K-bit packet, the radio is
\begin{equation}
E_{rx}\left(k,d \right) = \emph{ KE_{elec} }.
\label{eq4-ch1}
\end{equation}
-\noindent The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
+\noindent The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set to $E_{DA}$ = 5 nJ/bit.
\indent The radio energy dissipation model considers only the energy consumed by the communication part of the sensor node. However, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by other parts inside the sensor node such as computation and sensing units.
-In this dissertation, we developed another energy consumption model that based on \cite{ref112}.
+\textbf{In this dissertation, we have based the energy consumption model on \cite{ref112}. This model will be detailed in Section \ref{ch4:sec:04:03}}.
%\subsection{Our Energy Consumption Model:}
%\label{ch1:sec9:subsec2}
